@article{
 title = {Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-In Hybrid Electric Buses},
 type = {article},
 year = {2017},
 identifiers = {[object Object]},
 keywords = {Control theory,distributed systems},
 volume = {14},
 id = {4bcde3c0-237e-3928-95c8-53843e857256},
 created = {2017-12-02T16:14:48.277Z},
 file_attached = {false},
 profile_id = {7cfba06b-2407-3c8f-b729-5e0599e7b0fc},
 last_modified = {2017-12-02T16:14:48.277Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2016 IEEE. We discuss a recently introduced ECO-driving concept known as smart procurement of naturally generated energy (SPONGE) in the context of plug-in hybrid electric buses. Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realize SPONGE are discussed, paying attention to the privacy implications of the underlying optimization problems. Note to Practitioners-In this paper, we present a new idea for ECO-driving for buses. It is an Internet of Things concept-that instead of connecting devices in space, connects devices in time via forecasting engines. Basically, a bus uses knowledge of the available energy at the next charging step, to optimize its performance beforehand. The system can be implemented using available (free) forecasting engines, and existing distributed optimization tools. A sample implementation is described using a Toyota plug-in Prius (as a proxy for a hybrid bus). Apart from the forecasting and optimization analytics, the only additional work needed was the development of an interface unit to control electric vehicle mode of the vehicle, and the development of a smart-phone app. Future work will investigate the impacts of our approach on the grid, the integration of the ideas into the hybrid drive cycle, and using driver behavior as an input into the design of the utility functions.},
 bibtype = {article},
 author = {Naoum-Sawaya, J. and Crisostomi, E. and Liu, M. and Gu, Y. and Shorten, R.},
 journal = {IEEE Transactions on Automation Science and Engineering},
 number = {2}
}

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We discuss a recently introduced ECO-driving concept known as smart procurement of naturally generated energy (SPONGE) in the context of plug-in hybrid electric buses. Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realize SPONGE are discussed, paying attention to the privacy implications of the underlying optimization problems. Note to Practitioners-In this paper, we present a new idea for ECO-driving for buses. It is an Internet of Things concept-that instead of connecting devices in space, connects devices in time via forecasting engines. Basically, a bus uses knowledge of the available energy at the next charging step, to optimize its performance beforehand. The system can be implemented using available (free) forecasting engines, and existing distributed optimization tools. A sample implementation is described using a Toyota plug-in Prius (as a proxy for a hybrid bus). Apart from the forecasting and optimization analytics, the only additional work needed was the development of an interface unit to control electric vehicle mode of the vehicle, and the development of a smart-phone app. Future work will investigate the impacts of our approach on the grid, the integration of the ideas into the hybrid drive cycle, and using driver behavior as an input into the design of the utility functions.","bibtype":"article","author":"Naoum-Sawaya, J. and Crisostomi, E. and Liu, M. and Gu, Y. and Shorten, R.","journal":"IEEE Transactions on Automation Science and Engineering","number":"2","bibtex":"@article{\n title = {Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-In Hybrid Electric Buses},\n type = {article},\n year = {2017},\n identifiers = {[object Object]},\n keywords = {Control theory,distributed systems},\n volume = {14},\n id = {4bcde3c0-237e-3928-95c8-53843e857256},\n created = {2017-12-02T16:14:48.277Z},\n file_attached = {false},\n profile_id = {7cfba06b-2407-3c8f-b729-5e0599e7b0fc},\n last_modified = {2017-12-02T16:14:48.277Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {© 2016 IEEE. 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